Kutambua Viputo vya Kimantiki katika Soko la Fedha la Iran: Njia ya Mabadiliko ya Mkoa wa Markov

1. Utangulizi

This study aims to investigate the existence and dynamic evolution of rational speculative bubbles in Iran's unofficial foreign exchange market. The foreign exchange market is a crucial component of any economy, directly affecting competitiveness, trade, investment, and inflation. In Iran, this market is characterized by high volatility, heavily influenced by oil revenue shocks, economic sanctions, and speculative activities. The core issue addressed in this paper is the deviation of the exchange rate from its fundamental value, which, if left unaddressed by policymakers, could lead to a currency crisis. This paper aims to identify these bubble periods using advanced econometric models, providing early warning signals for more effective monetary and exchange rate policy interventions.

2. Ukaguzi wa Fasihi na Mfumo wa Nadharia

2.1. Rational Bubbles in Asset Pricing

理性泡沫的概念源于资产定价文献，指资产的市场价格持续偏离基于预期未来现金流现值的基本价值。在理性泡沫中，交易者愿意支付高于基本价值的价格，因为他们预期未来能以更高的价格卖出（Blanchard & Watson, 1982）。这种自我实现的预言可能导致价格呈爆炸性增长。

2.2. Exchange Rate Determination and Market Failure

Miundo ya jadi ya uchumi (kama njia ya fedha, njia ya usawa ya mfuko wa mali) mara nyingi haziwezi kuelezea mabadiliko ya kati na mafupi ya ubadilishaji wa fedha, hii ni fumbo lililosisitizwa na Meese na Rogoff (1983). Ufadhili wa tabia unaletia vipengele kama hisia za wawekezaji, athari ya kufuata wengine, na mashambulizi ya ununuzi wa hisa kama sababu kuu za kuendesha. "Fumbo la Kutenganishwa" linaonyesha kuwa ubadilishaji wa fedha mara nyingi huendeshwa na mambo mengine zaidi ya msingi wa kawaida.

2.3. Mazingira ya Soko la Fedha la Kigeni nchini Iran

Iran's foreign exchange market operates within a multi-tiered system, encompassing official exchange rates, secondary market rates, and unofficial (black market) rates. The unofficial market, driven by supply-demand imbalances, capital flight, and expectations regarding sanctions and oil revenues, is highly susceptible to bubble formation. The central bank typically intervenes to stabilize the market by selling foreign currency obtained from oil sales, but it can be overwhelmed by speculative pressures.

3. Mbinu na Uundaji wa Mfano

3.1. Markov Regime-Switching Model with Time-Varying Transition Probabilities

This study employs a Markov regime-switching model, a type of regime-switching model where the economy can be in different states (e.g., calm, boom, crash). Its key innovation lies in the use oftime-varying transition probabilitiesUnlike the standard MS model with fixed state transition probabilities, the TVTP variant allows the probability of transitioning from one regime to another to depend on observed economic variables (e.g., sanction intensity, changes in foreign exchange reserves). This makes the model more realistic in capturing the impact of policy changes and external shocks on market sentiment.

3.2. Model Specification and Bubble Identification

The model specifies three distinct regimes for the unofficial exchange rate ($s_t$):

1. Explosive Regime: Characterized by a rapid rise (depreciation) in the exchange rate, signaling a bubble.
2. Tranquil Regime: Inayojulikana kwa mwenendo mpole na thabiti.
3. Mfumo wa eneo la kuvunjika: Inayojulikana kwa marekebisho makali au kushuka kwa thamani ya sarafa baada ya kuvunjika kwa povu.

3.3. Data and Variables

The analysis uses monthly data from March 2010 to September 2018. The main variable is the unofficial market exchange rate of the US dollar to the Iranian rial. Transition probabilities are modeled as a function of the following variables:

• Sanctions Index: Vigezo vya mbadala vya shinikizo la uchumi la nje, vitazidisha mahitaji ya sarafu salama.
• Mabadiliko ya akiba ya kigeni: Inaonyesha uwezo wa Benki Kuu kuingilia kati na kulinda sarafu yake ya ndani.

4. Empirical Results and Analysis

4.1. Model Estimation and Regime Classification

The MS-TVTP model was successfully estimated. The smoothed probability plot clearly demonstrates the model's ability to partition the timeline into three distinct regimes. The model exhibits high accuracy in pinpointing periods of market stress.

4.2. Utambuzi wa Vipindi vya Bubble

Mfano huu ulitambua vipindi kadhaa vya povu vilivyolipuka kwenye mbadala wa sarafu isiyo rasmi ya dola/riali:

• Mei 2011 (5/90)
• Septemba-Oktober 2011 (9/90 – 10/90)
• Julai 2012 (7/91)
• Oktoba 2012 - Novemba 2012 (10/91 – 11/91)
• Aprili 2013 (4/92)
• Januari 2018 - Juni 2018 (1/97 – 6/97)

4.3. Utoaji wa Viashiria vya Onyo

Sanctions index ilithibitika kuwa kichocheo muhimu sana cha mabadiliko ya hali hadi eneo la mlipuko. Kupanda kwa index hiyo kuliongeza uwezekano wa soko kubadilika kutoka hali ya utulivu au kufurika hadi hali ya povu la mlipuko. Mabadiliko katika hifadhi ya kigeni pia yalikuwa muhimu; kupungua kwa hifadhi (kudhoofisha uwezo wa kuingilia kati) kuliongeza uwezekano wa kuingia au kubaki katika hali ya mlipuko. Hali ya kufurika mara nyingi hufuata vipindi vya mlipuko, na mara nyingi hufanyika wakati wa uingiliaji kati mkubwa wa benki kuu au kupungua kwa muda wa msongo wa soko.

5. Majadiliano na Ufafanuzi

5.1. Ufahamu Muhimu na Mfuatano wa Mantiki

Ufahamu Mkuu: Thamani ya Rial ya Irani haijaamuliwi tu na bei ya mafuta au usambazaji wa fedha; ni pia uwanja wa kisaikolojia. Uzuri wa makala hii ni kuweka hili rasmi: ubadilishaji wa sarafu niMfumo wa ImaniSanctions not only strangle the economy but also trigger a shift in market psychology from "calm" to "panic," thereby initiating a rational bubble. At this point, buying dollars becomes a survival strategy, not speculative gambling.

Logical Thread: The argumentation process is extremely sophisticated. 1) The standard model fails (the Meese-Rogoff puzzle). 2) Therefore, expectations and regime factors are incorporated. 3) Sanctions and reserve changes are observable proxy variables that alter these expectations. 4) The MS-TVTP model captures this, identifying the precise bubble window. The logic is tight: if you can model the transition mechanism, you can predict the bubble.

5.2. Advantages and Limitations of the Method

Faida:

• Hekima ya Kimaadili: Iliaepuka kazi ya kutathmini "msingi" katika uchumi uliopotoshwa kama wa Irani, badala yake inazingatia mambo yanayoweza kutazamwa kwa urahisi zaidi.Mchakato wa kupotoka。
• Matokeo yaliyo tayari kwa sera: Modeli huo haisemi tu "kuna povu" tu; inasema "uwezekano wa kuingia kwenye povu mwezi ujao ni X%, unaendeshwa na kiwango cha vikwazo Y". Hii ni taarifa inayoweza kutekelezwa.
• Uthibitishaji wa kimajaribio: Vipindi vilivyotambuliwa vya povu vinapatana na mafarakano ya kihistoria, ikipa modeli uhalali wa uso wenye nguvu.

• Viashiria vya tahadhari ya kisanduku nyeusi: "Sanctions Index" ni kigezo kilichojengwa. Muundo na uzito wake ni muhimu sana, lakini unaweza kuwa na ubaguzi wa kibinafsi. Takataka inapoingia, takataka ndiyo inatoka.
• Kuchelewa nyuma ya ukweli: Mfano unakadiriwa kulingana na data ya kihistoria. Katika mzozo unaokua kwa kasi, ripoti za viashiria (kama mabadiliko ya akiba) zinaweza kuchelewa, na hivyo kupunguza matumizi ya wakati halisi.
• Dhana ya Ubusara: Mfumo wa "bubujiko la busara" unaweza kupunguza thamani ya hofu safi na tabia ya kufuata wengine, ambayo inaweza kuwa isiyo na busara, na kujithibitisha yenyewe kwa kasi inayozidi uwezo wa kukamata wa mfano wowote.

5.3. Actionable Recommendations for Policymakers

Kwa Benki Kuu ya Iran na Kamati ya Ustahimilivu wa Fedha, utafiti huu ni mwongozo wa ki-taktiki, sio zoezi la kitaaluma tu.

1. Fuatilia mabadiliko, usilenge viwango pekee: Shift the focus from the absolute level of exchange rates toThe probability of regime switching. A calm market under rising sanction pressure is a pre-explosion state.
2. Strategic reserve ammunition: Hifadhi ya fedha za kigeni ndiyo chombo kikuu cha kupambana na povu. Mfano unaonyesha kuingilia kati kunafaa zaidi katika hatua ya "kuanguka". Kutumia hifadhi wakati wa katikati ya povu la mlipuko (wakati hisia za soko zinapokuwa za kukata tamaa kabisa) hakuna faida. Wakati wa kuingilia kati unapaswa kuchaguliwa wakati wa kuchochea mabadiliko kutoka kwa mfumo wa eneo la mlipuko hadi mfumo wa eneo la kuanguka.
3. Kuweka usimamizi wa matarajio kama chombo kikuu cha sera: Kwa kuwa soko linaendeshwa na imani, mawasiliano na uaminifu ni muhimu kabisa. Sera ya uwazi na inayotegemea kanuni ya kuingilia kati husaidia kutia nanga matarajio, na kupunguza uwezekano wa kugeukia mfumo wa eneo la mlipuko. Sera isiyo wazi au isiyo thabiti italeta matokeo kinyume.
4. Kujenga Mfumo wa Tahadhari ya Papo hapo: Fanya muundo huu uwe wa kutumika. Ingiza data ya papo hapo kuhusu mtiririko wa habari za vikwazo (kutumia teknolojia ya usindikaji wa lugha asilia kuchambua matangazo ya habari), makadirio ya hifadhi ya karibu papo hapo, na viashiria vya kina cha soko. Hii itaunda dashibodi ya kuzuia mafuriko.

6. Kiambatisho cha Kiufundi

6.1. Fomula za Hisabati

Kiini cha MS-TVTP model kinaweza kuwakilishwa kama ifuatavyo. Acha $s_t$ iwe logariti ya kiwango cha ubadilishaji wa sarafu kisichokuwa rasmi. Mchakato huo unatengenezwa kama:

$\Delta s_t = \mu(S_t) + \epsilon_t, \quad \epsilon_t \sim N(0, \sigma^2(S_t))$

Here $S_t \in \{1,2,3\}$ denotes the unobserved regime (1=calm, 2=explosive, 3=crash). Transitions between regimes are governed by a probability matrix $P_t$, where each element $p_{ij,t} = Pr(S_t = j | S_{t-1} = i)$ is time-varying.

These time-varying probabilities are modeled using a multinomial Logit specification:

$p_{ij,t} = \frac{\exp(\theta_{ij} + \beta_{ij}' Z_{t-1})}{\sum_{k=1}^{3} \exp(\theta_{ik} + \beta_{ik}' Z_{t-1})}$

Ambapo $Z_{t-1}$ ni vekta ya viashiria vya onyo kwa wakati $t-1$ (k.m., fahirisi ya vikwazo, mabadiliko ya akiba), na $\theta_{ij}, \beta_{ij}$ ni vigezo vinavyotakiwa kukadiriwa. Mpangilio huu hufanya uwezekano wa kubadili hadi eneo la povu kutegemea moja kwa moja shinikizo la uchumi linaloweza kutambuliwa.

6.2. Mfano wa Mfumo wa Uchambuzi

Mazingira: Mchambuzi wa Benki Kuu ya Irani anataka kutathmini hatari ya kuundwa kwa povu la upekuzi katika robo inayofuata.

Framework Application:

1. Data Input: Collect the latest values for the sanctions index (e.g., derived from news sentiment analysis of major Western media and government statements) and monthly foreign exchange reserve changes.
2. Model Query: Input these values into the estimated MS-TVTP model. The model uses the currently inferred regime state (from the latest exchange rate data) and the input $Z_t$ values.
3. Output Interpretation: The model outputs the probability of being in each of the three regimes in the next period.ProbabilityFor example:
   • $Pr(Calm) = 0.15$
   • $Pr(explosion) = 0.80$
   • $Pr(collapse) = 0.05$
4. Hitimisho linaloweza kutekelezwa: Uwezekano wa kuingia katika eneo la mlipuko ni 80%, hii ni ishara ya hatari. Ripoti ya mchambuzi itasisitiza kuwa, kutokana na shinikizo la juu la vikwazo na kupungua kwa akiba, soko lina uwezekano mkubwa wa kuingia katika awamu ya povu. Hii itasababisha mapendekezo ya kuwa Benki Kuu iandae mpango wa dharura, izingatie mawasiliano ya kwanza-kwanza ili kudhibiti matarajio, na kukagua mkakati wa utumiaji wa akiba.

7. Future Applications and Research Prospects

The methodology and insights of this study have broad applicability beyond the specific context of Iran.

• Other Sanctioned or Fragile Economies: This model can be applied to countries such as Venezuela, Russia, or Turkey, where geopolitical risks and capital flow volatility create similar dynamics. The key lies in identifying the correct local early-warning indicators (e.g., political stability index, commodity price volatility).
• Soko la Fedha za Kripto: Soko la Fedha za Kripto linajulikana kwa kuchochea mapovu yanayoendeshwa na hisia na habari za udhibiti. Kutumia mfano wa MS-TVTP wa faharasa za hisia za mitandao ya kijamii, matangazo ya udhibiti, na viashiria vya mnyororo, kunaweza kuwa na ufanisi mkubwa katika kutambua maeneo ya povu ya Bitcoin au Ethereum.
• Kwa Mchanganyiko na Masomo ya Mashine: Kazi za baadaye zinaweza kutumia kikaguzi cha kujifunza mashine (mfano, misitu ya nasibu, mtandao wa neva) badala ya usanidi wa uwezekano wa uhamisho wa Logit, ili kukamata uhusiano tata zaidi, usio na mstari kati ya viashiria na mabadiliko ya hali.
• Uundaji wa dashibodi ya wakati halisi: Hatua inayofuata ya kimantiki ni kujenga dashibodi ya programu inayopokea mtiririko wa data ya wakati halisi, inayoendesha muundo kwa uendelevu, na kuwataarifu wazalishaji sera kwa njia ya kuona kuhusu kuongezeka kwa uwezekano wa povu kama "ramani ya hali ya hewa ya utulivu wa kifedha".
• Uigaji wa sera: Muundo huu unaweza kutumika kuiga athari za vitendo mbalimbali vya sera (k.m., kuingiza akiba kwa kiwango kikubwa, mabadiliko ya viwango vya riba) kwenye uwezekano wa mabadiliko, na kusaidia kutathmini ufanisi unaoweza kukamilika wa zana za sera kabla ya utekelezaji.

8. References

1. Blanchard, O. J., & Watson, M. W. (1982). Bubbles, rational expectations and financial markets. In P. Wachtel (Ed.), Crises in the Economic and Financial Structure. Lexington Books.
2. Meese, R. A., & Rogoff, K. (1983). Empirical exchange rate models of the seventies: Do they fit out of sample? Jarida la Uchumi wa Kimataifa, 14(1-2), 3-24.
3. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.
4. Filardo, A. J. (1994). Business-cycle phases and their transitional dynamics. Journal of Business & Economic Statistics, 12(3), 299-308.
5. Taiebnia, A., Mehraara, M., & Akhtari, A. (2019). [伊朗非官方汇率市场中的理性泡沫与投机性攻击：基于时变转移概率的马尔可夫区制转换模型]. Scientific Research Quarterly Journal of Economic Research, 19(74), 111-164. (Original Persian Publication).
6. International Monetary Fund. (2023). Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER). Retrieved from IMF eLibrary.
7. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27. (Cited as an example of advanced modeling techniques applicable to regime detection).